Personalized fairness in recommendations has been attracting increasing attention from researchers. The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy. In this paper, we propose a novel Adaptive Fair Representation Learning (AFRL) model, which achieves a real personalized fairness due to its advantage of training only one model to adaptively serve different fairness requirements during inference phase. Particularly, AFRL treats fairness requirements as inputs and can learn an attribute-specific embedding for each attribute from the unfair user embedding, which endows AFRL with the adaptability during inference phase to determine the non-sensitive attributes under the guidance of the user's unique fairness requirement. To achieve a better trade-off between fairness and accuracy in recommendations, AFRL conducts a novel Information Alignment to exactly preserve discriminative information of non-sensitive attributes and incorporate a debiased collaborative embedding into the fair embedding to capture attribute-independent collaborative signals, without loss of fairness. Finally, the extensive experiments conducted on real datasets together with the sound theoretical analysis demonstrate the superiority of AFRL.

翻译：个性化公平推荐正日益受到研究者的关注。现有工作通常将由敏感属性集合表示的公平需求视为超参数，并通过从学习到的公平嵌入中完全移除敏感属性信息来追求极端公平，这面临两大挑战：属性组合爆炸导致的巨大训练成本，以及公平性与准确性之间的次优权衡。本文提出一种新颖的自适应公平表示学习（AFRL）模型，该模型具有仅需训练单一模型即可在推理阶段自适应满足不同公平需求的优势，从而实现了真正的个性化公平。具体而言，AFRL将公平需求作为输入，能够从不公平的用户嵌入中为每个属性学习属性特定嵌入，这使得AFRL在推理阶段具备自适应性，可在用户独特公平需求的指导下确定非敏感属性。为了在推荐中实现公平性与准确性之间更优的权衡，AFRL通过一种新颖的信息对齐方法，精确保留非敏感属性的判别信息，并将去偏协同嵌入融入公平嵌入中，以捕获与属性无关的协同信号，且不损失公平性。最后，在真实数据集上进行的大量实验，结合扎实的理论分析，证明了AFRL的优越性。